European Union and International Migrations
Bibliographic record
Abstract
This paper builds on a new data set measuring emigration stocks and rates by educational attainment for all the world countries and most dependent territories in 1990 and 2000 (Docquier and Marfouk [2005]). We analyze the impact of the European Union on the international mobility of skilled workers. Compared to other oecd countries, the average skills of eu 15 immigrants are low. However, by attracting an important proportion of African migrants, the eu 15 plays an important role in the brain drain debate. The eu 15 is an important source of brain drain for countries which are strongly concerned by human capital shortages. This result is confirmed by Kernel density estimates. Regarding exchanges of skilled workers with the other traditional immigration countries, the eu 15 experiences a large deficit. This deficit is compensated by importing human capital from developing countries. On the whole, the net effect is very small compared to the large gains observed in the us, in Canada and Australia.Classification JEL: F22, J61.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".